On all the datasets, our model achieves state-of-the-art performance in every setting. It improves the previous best score by a large margin, from 3.8% to 7.1% at Recall@1 with the 128 embedding dimensions. We note that our model clearly surpasses all existing models whose embedding dimension is 4 times higher on all the datasets, sometimes at 8 times higher. Moreover, on the CUB dataset, it even outperforms some of the supervised learning methods, e.g., ABIER [1]} and MS [2]}, given the same embedding networks. These results suggest that our framework allows the model to learn compact yet effective embedding spaces by providing high-quality synthetic supervision.
Last but not least, STML enables more efficient learning than existing pseudo labeling methods due to no use of off-the-shelf algorithms, especially on large-scale SOP dataset.
